Tax Interactions with Asymmetric Information and Nonlinear Instruments

Tax Interactions with Asymmetric Information and Nonlinear Instruments

Relevant Space Partitioning for Collaborative Generalisation

workshopInternational audienc

What Is the Level of Detail of OpenStreetMap?

workshopInternational audienc

Social Welfare to Assess the Global Legibility of a Generalized Map

International audienceCartographic generalization seeks to summarize geographical information from a geo-database to produce a less detailed and readable map. The specifications of a legible map are translated into a set of constraints to guide the generalization process and evaluate it. The global evaluation of the map, or of a part of it, consisting in aggregating all the single constraints satisfactions, is still to tackle for the generalization community. This paper deals with the use of the social welfare theory to handle the aggregation of the single satisfactions on the map level. The social welfare theory deals with the evaluation of the economical global welfare of a society, based on the individual welfare. Different social welfare orderings are adapted to generalization, compared and some are chosen for several generalization use cases. Experiments with topographic maps are carried out to validate the choices

Finding the Oasis in the Desert Fog? Understanding Multi- Scale Map Reading

workshopInternational audienc

A Road Network Selection Process Based on Data Enrichment and Structure Detection

International audienceIn the context of geographical database generalisation, this paper deals with a generic process for road network selection. It is based on the geographical context that is made explicit and on the characteristic structure preservation. It relies on literature work that is adapted and gathered. The first step is to detect significant structures and patterns of the road network such as roundabouts or highway interchanges. It allows to enrich the initial dataset with explicit geographic structures that were implicit in initial data. It helps both to explicit the geographical context and to preserve characteristic structures. Then, this enrichment is used as knowledge input for the following step that is the selection of roads in rural areas using graph theory techniques. After that, urban roads are selected by means of a block aggregation complex algorithm. Continuity between urban and rural areas is guaranteed by modelling continuity using strokes. Finally, the previously detected characteristic structures are typified to maintain their properties in the selected network. This automated process has been fully implemented on Clarity™ and tested on large datasets

Lessons Learned From Research on Multimedia Summarization

workshopInternational audienc

Deep Learning for Enrichment of Vector Spatial Databases: Application to Highway Interchange

Spatial analysis and pattern recognition with vector spatial data is particularly useful to enrich raw data. In road networks for instance, there are many patterns and structures that are implicit with only road line features, among which highway interchange appeared very complex to recognise with vector-based techniques. The goal is to find the roads that belong to an interchange, i.e. the slip roads and the highway roads connected to the slip roads. In order to go further than state-of-the-art vector-based techniques, this paper proposes to use raster-based deep learning techniques to recognise highway interchanges. The contribution of this work is to study how to optimally convert vector data into small images suitable for state-of-the-art deep learning models. Image classification with a convolutional neural network (i.e. is there an interchange in this image or not?) and image segmentation with a u-net (i.e. find the pixels that cover the interchange) are experimented and give results way better than existing vector-based techniques in this specific use case

Diffusion of active tracers in fluctuating fields

The problem of a particle diffusion in a fluctuating scalar field is studied. In contrast to most studies of advection diffusion in random fields we analyze the case where the particle position is also coupled to the dynamics of the field. Physical realizations of this problem are numerous and range from the diffusion of proteins in fluctuating membranes and the diffusion of localized magnetic fields in spin systems. We present exact results for the diffusion constant of particles diffusing in dynamical Gaussian fields in the adiabatic limit where the field evolution is much faster than the particle diffusion. In addition we compute the diffusion constant perturbatively, in the weak coupling limit where the interaction of the particle with the field is small, using a Kubo-type relation. Finally we construct a simple toy model which can be solved exactly.Comment: 13 pages, 1 figur